


import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


snake_case__				:	List[Any]					       =					logging.get_logger(__name__)

enable_full_determinism()





class   SCREAMING_SNAKE_CASE_			(__lowerCAmelCase						,       __lowerCAmelCase						,       unittest.TestCase						):




       '''simple docstring'''



       _a   							=							UNetaDModel
       _a   							=							'''sample'''



       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->Tuple:
          lowerCamelCase_						:   Optional[int]		= 4
          lowerCamelCase_						:   str		= 3
          lowerCamelCase_						:   Union[str, Any]		= (32, 32)

          lowerCamelCase_						:   Optional[int]		= floats_tensor((batch_size, num_channels) + sizes							).to(_lowerCamelCase							)
          lowerCamelCase_						:   List[str]		= torch.tensor([10]							).to(_lowerCamelCase							)

          return {"sample": noise, "timestep": time_step}



       @property
       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Union[str, Any]:
          return (3, 32, 32)



       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->Tuple:
          return (3, 32, 32)




       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->Optional[int]:
          lowerCamelCase_						:   Optional[Any]		= {
              """block_out_channels""": (32, 64),
              """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
              """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
              """attention_head_dim""": 3,
              """out_channels""": 3,
              """in_channels""": 3,
              """layers_per_block""": 2,
              """sample_size""": 32,
          }
          lowerCamelCase_						:   Optional[Any]		= self.dummy_input
          return init_dict, inputs_dict







class   SCREAMING_SNAKE_CASE_			(__lowerCAmelCase						,       __lowerCAmelCase						,       unittest.TestCase						):




       '''simple docstring'''



       _a   							=							UNetaDModel
       _a   							=							'''sample'''



       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->Tuple:
          lowerCamelCase_						:   Optional[Any]		= 4
          lowerCamelCase_						:   str		= 4
          lowerCamelCase_						:   List[Any]		= (32, 32)

          lowerCamelCase_						:   int		= floats_tensor((batch_size, num_channels) + sizes							).to(_lowerCamelCase							)
          lowerCamelCase_						:   Any		= torch.tensor([10]							).to(_lowerCamelCase							)

          return {"sample": noise, "timestep": time_step}



       @property
       def 				_lowerCAmelCase							(						self				:				Dict							)					->int:
          return (4, 32, 32)



       @property
       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Any:
          return (4, 32, 32)



       def 				_lowerCAmelCase							(						self				:				int							)					->Any:
          lowerCamelCase_						:   Optional[Any]		= {
              """sample_size""": 32,
              """in_channels""": 4,
              """out_channels""": 4,
              """layers_per_block""": 2,
              """block_out_channels""": (32, 64),
              """attention_head_dim""": 32,
              """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
              """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
          }
          lowerCamelCase_						:   List[str]		= self.dummy_input
          return init_dict, inputs_dict



       def 				_lowerCAmelCase							(						self				:				Dict							)					->Optional[int]:
          lowerCamelCase_,	lowerCamelCase_						:   str		= UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""		,				output_loading_info=_lowerCamelCase							)

          self.assertIsNotNone(_lowerCamelCase							)
          self.assertEqual(len(loading_info["""missing_keys"""]							)		,				0							)

          model.to(_lowerCamelCase							)
          lowerCamelCase_						:   Dict		= model(**self.dummy_input							).sample

          assert image is not None, "Make sure output is not None"



       @unittest.skipIf(torch_device != """cuda"""		,				"""This test is supposed to run on GPU"""							)
       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->List[Any]:
          lowerCamelCase_,	lowerCamelCase_						:   List[str]		= UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""		,				output_loading_info=_lowerCamelCase							)
          model.to(_lowerCamelCase							)
          lowerCamelCase_						:   Dict		= model(**self.dummy_input							).sample

          assert image is not None, "Make sure output is not None"



       @unittest.skipIf(torch_device != """cuda"""		,				"""This test is supposed to run on GPU"""							)
       def 				_lowerCAmelCase							(						self				:				Dict							)					->Tuple:
          # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
          lowerCamelCase_,	lowerCamelCase_						:   int		= UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""		,				output_loading_info=_lowerCamelCase							)
          model_accelerate.to(_lowerCamelCase							)
          model_accelerate.eval()

          lowerCamelCase_						:   Tuple		= torch.randn(
              1		,				model_accelerate.config.in_channels		,				model_accelerate.config.sample_size		,				model_accelerate.config.sample_size		,				generator=torch.manual_seed(0							)		,				)
          lowerCamelCase_						:   Optional[int]		= noise.to(_lowerCamelCase							)
          lowerCamelCase_						:   str		= torch.tensor([10] * noise.shape[0]							).to(_lowerCamelCase							)

          lowerCamelCase_						:   Dict		= model_accelerate(_lowerCamelCase		,				_lowerCamelCase							)["""sample"""]

          # two models don't need to stay in the device at the same time
          del model_accelerate
          torch.cuda.empty_cache()
          gc.collect()

          lowerCamelCase_,	lowerCamelCase_						:   List[str]		= UNetaDModel.from_pretrained(
              """fusing/unet-ldm-dummy-update"""		,				output_loading_info=_lowerCamelCase		,				low_cpu_mem_usage=_lowerCamelCase							)
          model_normal_load.to(_lowerCamelCase							)
          model_normal_load.eval()
          lowerCamelCase_						:   Union[str, Any]		= model_normal_load(_lowerCamelCase		,				_lowerCamelCase							)["""sample"""]

          assert torch_all_close(_lowerCamelCase		,				_lowerCamelCase		,				rtol=1e-3							)




       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->int:
          lowerCamelCase_						:   Tuple		= UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""							)
          model.eval()
          model.to(_lowerCamelCase							)

          lowerCamelCase_						:   List[Any]		= torch.randn(
              1		,				model.config.in_channels		,				model.config.sample_size		,				model.config.sample_size		,				generator=torch.manual_seed(0							)		,				)
          lowerCamelCase_						:   Optional[int]		= noise.to(_lowerCamelCase							)
          lowerCamelCase_						:   Any		= torch.tensor([10] * noise.shape[0]							).to(_lowerCamelCase							)

          with torch.no_grad():
             lowerCamelCase_						:   Tuple		= model(_lowerCamelCase		,				_lowerCamelCase							).sample

          lowerCamelCase_						:   str		= output[0, -1, -3:, -3:].flatten().cpu()
          # fmt: off
          lowerCamelCase_						:   Tuple		= torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]							)
          # fmt: on

          self.assertTrue(torch_all_close(_lowerCamelCase		,				_lowerCamelCase		,				rtol=1e-3							)							)







class   SCREAMING_SNAKE_CASE_			(__lowerCAmelCase						,       __lowerCAmelCase						,       unittest.TestCase						):




       '''simple docstring'''



       _a   							=							UNetaDModel
       _a   							=							'''sample'''



       @property
       def 				_lowerCAmelCase							(						self				:				Dict		,				__a				:				str=(32, 32)							)					->List[str]:
          lowerCamelCase_						:   Optional[int]		= 4
          lowerCamelCase_						:   int		= 3

          lowerCamelCase_						:   Tuple		= floats_tensor((batch_size, num_channels) + sizes							).to(_lowerCamelCase							)
          lowerCamelCase_						:   Tuple		= torch.tensor(batch_size * [10]							).to(dtype=torch.intaa		,				device=_lowerCamelCase							)

          return {"sample": noise, "timestep": time_step}



       @property
       def 				_lowerCAmelCase							(						self				:				Any							)					->str:
          return (3, 32, 32)



       @property
       def 				_lowerCAmelCase							(						self				:				Any							)					->List[Any]:
          return (3, 32, 32)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->int:
          lowerCamelCase_						:   Union[str, Any]		= {
              """block_out_channels""": [32, 64, 64, 64],
              """in_channels""": 3,
              """layers_per_block""": 1,
              """out_channels""": 3,
              """time_embedding_type""": """fourier""",
              """norm_eps""": 1e-6,
              """mid_block_scale_factor""": math.sqrt(2.0							),
              """norm_num_groups""": None,
              """down_block_types""": [
                  """SkipDownBlock2D""",
                  """AttnSkipDownBlock2D""",
                  """SkipDownBlock2D""",
                  """SkipDownBlock2D""",
              ],
              """up_block_types""": [
                  """SkipUpBlock2D""",
                  """SkipUpBlock2D""",
                  """AttnSkipUpBlock2D""",
                  """SkipUpBlock2D""",
              ],
          }
          lowerCamelCase_						:   str		= self.dummy_input
          return init_dict, inputs_dict



       @slow
       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->int:
          lowerCamelCase_,	lowerCamelCase_						:   Union[str, Any]		= UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""		,				output_loading_info=_lowerCamelCase							)
          self.assertIsNotNone(_lowerCamelCase							)
          self.assertEqual(len(loading_info["""missing_keys"""]							)		,				0							)

          model.to(_lowerCamelCase							)
          lowerCamelCase_						:   List[str]		= self.dummy_input
          lowerCamelCase_						:   Optional[Any]		= floats_tensor((4, 3) + (256, 256)							).to(_lowerCamelCase							)
          lowerCamelCase_						:   Tuple		= noise
          lowerCamelCase_						:   Union[str, Any]		= model(**_lowerCamelCase							)

          assert image is not None, "Make sure output is not None"



       @slow
       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->List[str]:
          lowerCamelCase_						:   List[str]		= UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""							)
          model.to(_lowerCamelCase							)

          lowerCamelCase_						:   Optional[Any]		= 4
          lowerCamelCase_						:   Tuple		= 3
          lowerCamelCase_						:   Optional[Any]		= (256, 256)

          lowerCamelCase_						:   List[str]		= torch.ones((batch_size, num_channels) + sizes							).to(_lowerCamelCase							)
          lowerCamelCase_						:   str		= torch.tensor(batch_size * [1e-4]							).to(_lowerCamelCase							)

          with torch.no_grad():
             lowerCamelCase_						:   str		= model(_lowerCamelCase		,				_lowerCamelCase							).sample

          lowerCamelCase_						:   Tuple		= output[0, -3:, -3:, -1].flatten().cpu()
          # fmt: off
          lowerCamelCase_						:   int		= torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]							)
          # fmt: on

          self.assertTrue(torch_all_close(_lowerCamelCase		,				_lowerCamelCase		,				rtol=1e-2							)							)



       def 				_lowerCAmelCase							(						self				:				str							)					->Tuple:
          lowerCamelCase_						:   List[Any]		= UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""							)
          model.to(_lowerCamelCase							)

          lowerCamelCase_						:   Optional[Any]		= 4
          lowerCamelCase_						:   int		= 3
          lowerCamelCase_						:   Optional[int]		= (32, 32)

          lowerCamelCase_						:   Optional[Any]		= torch.ones((batch_size, num_channels) + sizes							).to(_lowerCamelCase							)
          lowerCamelCase_						:   Any		= torch.tensor(batch_size * [1e-4]							).to(_lowerCamelCase							)

          with torch.no_grad():
             lowerCamelCase_						:   List[Any]		= model(_lowerCamelCase		,				_lowerCamelCase							).sample

          lowerCamelCase_						:   Union[str, Any]		= output[0, -3:, -3:, -1].flatten().cpu()
          # fmt: off
          lowerCamelCase_						:   Optional[int]		= torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]							)
          # fmt: on

          self.assertTrue(torch_all_close(_lowerCamelCase		,				_lowerCamelCase		,				rtol=1e-2							)							)




       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Dict:
          # not required for this model
          pass






"""simple docstring"""


from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   list[float]:




						'''simple docstring'''



						if radian_mode:
												return [magnitude * cos(__lowerCAmelCase							), magnitude * sin(__lowerCAmelCase							)]
						return [magnitude * cos(radians(__lowerCAmelCase							)							), magnitude * sin(radians(__lowerCAmelCase							)							)]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = 10**-1							)				->   bool:




						'''simple docstring'''



						lowerCamelCase__   =cross(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =sum(__lowerCAmelCase							)
						return abs(__lowerCAmelCase							) < eps


if __name__ == "__main__":
	# Test to check if it works
	a				=array(
	    [
	        polar_force(718.4, 180 - 30),
	        polar_force(879.54, 45),
	        polar_force(100, -90),
	    ]
	)

	a				=array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem 1 in image_data/2D_problems.jpg
	a				=array(
	    [
	        polar_force(30 * 9.81, 15),
	        polar_force(215, 180 - 45),
	        polar_force(264, 90 - 30),
	    ]
	)

	a				=array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem in image_data/2D_problems_1.jpg
	a				=array([[0, -2000], [0, -1200], [0, 1_5600], [0, -1_2400]])

	a				=array([[0, 0], [6, 0], [10, 0], [12, 0]])

	assert in_static_equilibrium(forces, location)

	import doctest

	doctest.testmod()


